code: def evaluate_tflite_model(tflite_model, test_ds):
      # Initialize TFLite interpreter using the model.
      interpreter = tf.lite.Interpreter(model_content=tflite_model)
      interpreter.allocate_tensors()
      input_tensor_index = interpreter.get_input_details()[0]["index"]
      output = interpreter.tensor(
          interpreter.get_output_details()[0]["index"]
      )
  
      # Run predictions on every image in the "test" dataset.
      prediction_digits = []
      labels = []
      for image, one_hot_label in test_ds:
          interpreter.set_tensor(input_tensor_index, image)
  
          # Run inference.
          interpreter.invoke()
  
          # Post-processing: for each batch dimension and find the digit with highest
          # probability.
          digits = np.argmax(output(), axis=1)
          prediction_digits.extend(digits)
          labels.extend(np.argmax(one_hot_label, axis=1))
  
      # Compare prediction results with ground truth labels to calculate accuracy.
      accurate_count = 0
      for index in range(len(prediction_digits)):
          if prediction_digits[index] == labels[index]:
              accurate_count += 1
      accuracy = accurate_count * 1.0 / len(prediction_digits)
      return accuracy
  
file_path: jax/jax/experimental/jax2tf/examples/tflite/mnist/mnist.py
imports: import logging
  from absl import app
  from absl import flags
  from jax.experimental import jax2tf
  from jax.experimental.jax2tf.examples import mnist_lib
  import numpy
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate_tflite_model(tflite_model, test_ds) and returns: accuracy